Supervised Segmentation of Microelectrode Recording Artifacts Using Power Spectral Density

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00233642" target="_blank" >RIV/68407700:21230/15:00233642 - isvavai.cz</a>
Result on the web
<a href="http://emb.citengine.com/event/embc-2015/paper-details?pdID=4502" target="_blank" >http://emb.citengine.com/event/embc-2015/paper-details?pdID=4502</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC.2015.7318661" target="_blank" >10.1109/EMBC.2015.7318661</a>

Result language
angličtina
Original language name
Supervised Segmentation of Microelectrode Recording Artifacts Using Power Spectral Density
Original language description
Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close tothe accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.
Czech name
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Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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